Name
Abstract | ||
|---|---|---|
Assisted living homes aim to deploy tools to promote better living of elderly population. One of such tools is assistive robotics to perform tasks a human carer would normally be required to perform. For assistive robots to perform activities without explicit programming, a major requirement is learning and classifying activities while it observes a human carry out the activities. This work proposes a human activity learning and classification system from features obtained using 3D RGB-D data. Different classifiers are explored in this approach and the system is evaluated on a publicly available data set, showing promising results which is capable of improving assistive robots performance in living environments. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-66939-7_22 | ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS |
Keywords | Field | DocType |
Activity recognition,Activity classification,Assistive robotics | Population,Activity classification,Activity recognition,Transfer of learning,Human–computer interaction,Artificial intelligence,Engineering,Robot,Multimedia,Robotics | Conference |
Volume | ISSN | Citations |
650 | 2194-5357 | 2 |
PageRank | References | Authors |
0.37 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Ada Adama | 1 | 6 | 2.13 |
| Lofti A. Zadeh | 2 | 14527 | 3847.07 |
| Caroline S. Langensiepen | 3 | 35 | 14.29 |
| Kevin Lee | 4 | 340 | 27.53 |